Best AI tools for< Recommendation System Developer >

Shaped is an AI tool designed to provide relevant recommendations and search results to increase engagement, conversion, and revenue. It offers a configurable system that adapts in real-time, with features such as easy set-up, real-time adaptability, state-of-the-art model library, high customizability, and explainable results. Shaped is suitable for technical teams and offers white-glove support. It specializes in real-time ranking systems and supports multi-modal unstructured data understanding. The tool ensures secure infrastructure and has advantages like increased redemption rate, average order value, and diversity.

Balik Games is a dynamic company specializing in developing innovative games and apps that cater to various interests and needs. With a focus on quality, diversity, and user experience, Balik Games is committed to creating fun, engaging, and easy-to-use products that deliver maximum value to customers. Their app offers a unique combination of ASMR soundscapes and a personalized recommendation system powered by AI, providing a customized experience tailored to individual preferences. Two of their popular games include Blocks!, a puzzle game challenging problem-solving skills, and Swipe Kingdom, a card-based game where players take on the role of a Viking King.

NVIDIA is a world leader in artificial intelligence computing. The company's products and services are used by businesses and governments around the world to develop and deploy AI applications. NVIDIA's AI platform includes hardware, software, and tools that make it easy to build and train AI models. The company also offers a range of cloud-based AI services that make it easy to deploy and manage AI applications. NVIDIA's AI platform is used in a wide variety of industries, including healthcare, manufacturing, retail, and transportation. The company's AI technology is helping to improve the efficiency and accuracy of a wide range of tasks, from medical diagnosis to product design.

Webb.ai is an AI-powered platform that offers automated troubleshooting for Kubernetes. It is designed to assist users in identifying and resolving issues within their Kubernetes environment efficiently. By leveraging AI technology, Webb.ai provides insights and recommendations to streamline the troubleshooting process, ultimately improving system reliability and performance. The platform is user-friendly and caters to both beginners and experienced users in the field of Kubernetes management.

Shaped is a cloud-based platform that provides APIs and tools for building and deploying ranking systems. It offers a variety of features to help developers quickly and easily create and manage ranking models, including a multi-connector SQL interface, a real-time feature store, and a library of pre-built models. Shaped is designed to be scalable, cost-efficient, and easy to use, making it a great option for businesses of all sizes.

Wasps is an AI code review tool that integrates seamlessly into VSCode, providing developers with a fast and efficient way to understand their codebase, detect and fix code issues using AI and Gitsecure. With Wasps, developers can identify and fix buggy & vulnerable code in minutes, receive clear and actionable feedback driven by deep analysis, and get recommendations for potential issues and improvements within their codebase. The tool allows developers to keep coding as usual while Wasps analyzes their code for them, making it easier to maintain code quality and keep bugs out of their code.

MiMi is a website intelligence tool that uses AI to enhance the user experience and drive sales. It offers a range of features including semantic search, chatbot, recommendations, virtual assistant, dynamic pricing, and automation. MiMi's AI engine can automatically learn and update knowledge from your site to provide an AI chatbot that can answer questions from visitors automatically. The machine learning algorithms can also learn from your site products and visitor behavior to bring recommender systems for your site. MiMi's AI algorithm serves as a virtual sales assistant, assisting websites in making flexible and tailored pricing decisions for each customer based on their behavior.

Ongkanon is an AI-powered chatbot designed to enhance daily interactions through intelligent conversations. It leverages advanced AI technology to engage in meaningful and contextual discussions, providing a personalized experience tailored to each user's preferences. Ongkanon's long-term memory capability allows it to retain context from previous conversations, resulting in more coherent and engaging interactions.

ResumeWiz is an AI-powered resume builder that allows users to create professional resumes quickly and easily. With a user-friendly interface, ResumeWiz guides users through the resume building process, providing templates and recommendations to optimize the resume for Applicant Tracking Systems (ATS). Users can input personal information, work experience, education, and skills into the AI-powered form and receive an ATS score to understand how well their resume will perform. The platform offers the option to download the resume as a PDF or PNG file for convenient sharing.

Amazon Science is a research and development organization within Amazon that focuses on developing new technologies and products in the fields of artificial intelligence, machine learning, and computer science. The organization is home to a team of world-renowned scientists and engineers who are working on a wide range of projects, including developing new algorithms for machine learning, building new computer vision systems, and creating new natural language processing tools. Amazon Science is also responsible for developing new products and services that use these technologies, such as the Amazon Echo and the Amazon Fire TV.

Tech Xplore is a leading source of science and technology news, covering the latest breakthroughs in research and innovation across a wide range of disciplines, including artificial intelligence, robotics, computer science, and more. The website provides in-depth articles, interviews with experts, and up-to-date information on the latest developments in the field of AI and its applications.

ColdIQ is an AI-powered sales prospecting tool that helps B2B companies with revenue above $100k/month to build outbound systems that sell for them. The tool offers end-to-end cold outreach campaign setup and management, email infrastructure setup and warmup, audience research and targeting, data scraping and enrichment, campaigns optimization, sending automation, sales systems implementation, training on tools best practices, sales tools recommendations, free gap analysis, sales consulting, and copywriting frameworks. ColdIQ leverages AI to tailor messaging to each prospect, automate outreach, and flood calendars with opportunities.

EXCELR8 is an AI-infused platform designed to help leaders and teams improve performance and retention to empower sustainable growth. It offers a range of tools and features such as performance and engagement surveys, AI-powered insights and data analysis, tailored learning and development, goals and action planning dashboards, and performance and retention management systems. The platform provides clear and actionable data, specific recommendations for immediate implementation, and personalized development opportunities. EXCELR8 aims to revolutionize the way organizations build and retain high-performance teams by leveraging AI technology and science-backed tools.

ICD AI is an advanced artificial intelligence tool designed to assist healthcare professionals in accurately assigning diagnostic codes to patient records. The tool utilizes machine learning algorithms to analyze medical data and suggest appropriate ICD codes, streamlining the coding process and reducing errors. With its user-friendly interface and robust features, ICD AI is revolutionizing medical coding practices and improving efficiency in healthcare facilities.

JobsRemote.ai is a free artificial intelligence-based platform that offers a curated selection of remote job-friendly tools to enhance the remote work experience. Users can browse thousands of handpicked tools suitable for remote jobs, ensuring high-quality recommendations without ads, scams, or junk listings. The platform focuses on providing legitimate and suitable job listings from top companies worldwide, streamlining the application process for job seekers. With a user-friendly interface and personalized recommendations, JobsRemote.ai aims to connect remote professionals with quality remote job opportunities efficiently.

Linkter is the #1 AI internal linking tool designed for SEO superstars. It automates the process of building strategic internal links, saving SEOs hundreds of hours of manual work. With advanced features like an AI recommendation system, anchor text manager, and custom AI implementation, Linkter enhances SEO rankings, indexation, and user experience. The tool revolutionizes website optimization by streamlining internal linking tasks and improving overall website visibility and performance.

Critique is an AI tool that redefines browsing by offering autonomous fact-checking, informed question answering, and a localized universal recommendation system. It automatically critiques comments and posts on platforms like Reddit, Youtube, and Linkedin by vetting text on any website. The tool cross-references and analyzes articles in real-time, providing vetted and summarized information directly in the user's browser.

Swipe Insight is a mobile application that provides users with daily updates on digital marketing and analytics trends, news, and strategies. The app features a personalized feed that adapts to the user's preferences, intelligent insights that distill complex topics into concise summaries, and a curated selection of content from over 100 trusted sources. Swipe Insight is designed to help users stay ahead in the industry with just minutes of reading per day.

Gift Recommender is an AI-powered application designed to assist users in finding the perfect gift for their loved ones. By providing basic information about the recipient such as name, age, gender, price range, and interests, the AI generates personalized gift recommendations. The system learns from user feedback to continuously improve its suggestions. While the AI provides recommendations, it acknowledges that the best gift is often something personal and encourages users to provide feedback for better training.

Big Vision provides consulting services in AI, computer vision, and deep learning. They help businesses build specific AI-driven solutions, create intelligent processes, and establish best practices to reduce human effort and enable faster decision-making. Their enterprise-grade solutions are currently serving millions of requests every month, especially in critical production environments.

This repository is a collection of papers and resources related to recommendation systems, focusing on foundation models, transferable recommender systems, large language models, and multimodal recommender systems. It explores questions such as the necessity of ID embeddings, the shift from matching to generating paradigms, and the future of multimodal recommender systems. The papers cover various aspects of recommendation systems, including pretraining, user representation, dataset benchmarks, and evaluation methods. The repository aims to provide insights and advancements in the field of recommendation systems through literature reviews, surveys, and empirical studies.

LLMRec is a PyTorch implementation for the WSDM 2024 paper 'Large Language Models with Graph Augmentation for Recommendation'. It is a novel framework that enhances recommenders by applying LLM-based graph augmentation strategies to recommendation systems. The tool aims to make the most of content within online platforms to augment interaction graphs by reinforcing u-i interactive edges, enhancing item node attributes, and conducting user node profiling from a natural language perspective.

The Next-Generation LLM-based Recommender Systems Survey is a comprehensive overview of the latest advancements in recommender systems leveraging Large Language Models (LLMs). The survey covers various paradigms, approaches, and applications of LLMs in recommendation tasks, including generative and non-generative models, multimodal recommendations, personalized explanations, and industrial deployment. It discusses the comparison with existing surveys, different paradigms, and specific works in the field. The survey also addresses challenges and future directions in the domain of LLM-based recommender systems.

Paper-Reading-ConvAI is a repository that contains a list of papers, datasets, and resources related to Conversational AI, mainly encompassing dialogue systems and natural language generation. This repository is constantly updating.

This project is a tutorial on developing large model applications for novice developers. It aims to provide a comprehensive introduction to large model development, focusing on Alibaba Cloud servers and integrating personal knowledge assistant projects. The tutorial covers the following topics: 1. **Introduction to Large Models**: A simplified introduction for novice developers on what large models are, their characteristics, what LangChain is, and how to develop an LLM application. 2. **How to Call Large Model APIs**: This section introduces various methods for calling APIs of well-known domestic and foreign large model products, including calling native APIs, encapsulating them as LangChain LLMs, and encapsulating them as Fastapi calls. It also provides a unified encapsulation for various large model APIs, such as Baidu Wenxin, Xunfei Xinghuo, and Zh譜AI. 3. **Knowledge Base Construction**: Loading, processing, and vector database construction of different types of knowledge base documents. 4. **Building RAG Applications**: Integrating LLM into LangChain to build a retrieval question and answer chain, and deploying applications using Streamlit. 5. **Verification and Iteration**: How to implement verification and iteration in large model development, and common evaluation methods. The project consists of three main parts: 1. **Introduction to LLM Development**: A simplified version of V1 aims to help beginners get started with LLM development quickly and conveniently, understand the general process of LLM development, and build a simple demo. 2. **LLM Development Techniques**: More advanced LLM development techniques, including but not limited to: Prompt Engineering, processing of multiple types of source data, optimizing retrieval, recall ranking, Agent framework, etc. 3. **LLM Application Examples**: Introduce some successful open source cases, analyze the ideas, core concepts, and implementation frameworks of these application examples from the perspective of this course, and help beginners understand what kind of applications they can develop through LLM. Currently, the first part has been completed, and everyone is welcome to read and learn; the second and third parts are under creation. **Directory Structure Description**: requirements.txt: Installation dependencies in the official environment notebook: Notebook source code file docs: Markdown documentation file figures: Pictures data_base: Knowledge base source file used

CoLLM is a novel method that integrates collaborative information into Large Language Models (LLMs) for recommendation. It converts recommendation data into language prompts, encodes them with both textual and collaborative information, and uses a two-step tuning method to train the model. The method incorporates user/item ID fields in prompts and employs a conventional collaborative model to generate user/item representations. CoLLM is built upon MiniGPT-4 and utilizes pretrained Vicuna weights for training.

Recommenders is a project under the Linux Foundation of AI and Data that assists researchers, developers, and enthusiasts in prototyping, experimenting with, and bringing to production a range of classic and state-of-the-art recommendation systems. The repository contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. It covers tasks such as preparing data, building models using various recommendation algorithms, evaluating algorithms, tuning hyperparameters, and operationalizing models in a production environment on Azure. The project provides utilities to support common tasks like loading datasets, evaluating model outputs, and splitting training/test data. It includes implementations of state-of-the-art algorithms for self-study and customization in applications.

KDB.AI is a time-based vector database that allows developers to build scalable, reliable, and real-time applications by providing advanced search, recommendation, and personalization for Generative AI applications. It supports multiple index types, distance metrics, top-N and metadata filtered retrieval, as well as Python and REST interfaces. The repository contains samples demonstrating various use-cases such as temporal similarity search, document search, image search, recommendation systems, sentiment analysis, and more. KDB.AI integrates with platforms like ChatGPT, Langchain, and LlamaIndex. The setup steps require Unix terminal, Python 3.8+, and pip installed. Users can install necessary Python packages and run Jupyter notebooks to interact with the samples.

A curated repository of code recipes, demos, and resources for basic and advanced Redis use cases in the AI ecosystem. It includes demos for ArxivChatGuru, Redis VSS, Vertex AI & Redis, Agentic RAG, ArXiv Search, and Product Search. Recipes cover topics like Getting started with RAG, Semantic Cache, Advanced RAG, and Recommendation systems. The repository also provides integrations/tools like RedisVL, AWS Bedrock, LangChain Python, LangChain JS, LlamaIndex, Semantic Kernel, RelevanceAI, and DocArray. Additional content includes blog posts, talks, reviews, and documentation related to Vector Similarity Search, AI-Powered Document Search, Vector Databases, Real-Time Product Recommendations, and more. Benchmarks compare Redis against other Vector Databases and ANN benchmarks. Documentation includes QuickStart guides, official literature for Vector Similarity Search, Redis-py client library docs, Redis Stack documentation, and Redis client list.

Colossal-AI is a deep learning system for large-scale parallel training. It provides a unified interface to scale sequential code of model training to distributed environments. Colossal-AI supports parallel training methods such as data, pipeline, tensor, and sequence parallelism and is integrated with heterogeneous training and zero redundancy optimizer.

Vespa is an open-source search and AI engine that provides a unified platform for building and deploying search and AI applications. Vespa sample applications showcase various use cases and features of Vespa, including basic search, recommendation, semantic search, image search, text ranking, e-commerce search, question answering, search-as-you-type, and ML inference serving.

AntSK is an AI knowledge base/agent built with .Net8+Blazor+SemanticKernel. It features a semantic kernel for accurate natural language processing, a memory kernel for continuous learning and knowledge storage, a knowledge base for importing and querying knowledge from various document formats, a text-to-image generator integrated with StableDiffusion, GPTs generation for creating personalized GPT models, API interfaces for integrating AntSK into other applications, an open API plugin system for extending functionality, a .Net plugin system for integrating business functions, real-time information retrieval from the internet, model management for adapting and managing different models from different vendors, support for domestic models and databases for operation in a trusted environment, and planned model fine-tuning based on llamafactory.

Deej-A.I. is an advanced machine learning project that aims to revolutionize music recommendation systems by using artificial intelligence to analyze and recommend songs based on their content and characteristics. The project involves scraping playlists from Spotify, creating embeddings of songs, training neural networks to analyze spectrograms, and generating recommendations based on similarities in music features. Deej-A.I. offers a unique approach to music curation, focusing on the 'what' rather than the 'how' of DJing, and providing users with personalized and creative music suggestions.

LearnOpenCV is a repository containing code for Computer Vision, Deep learning, and AI research articles shared on the blog LearnOpenCV.com. It serves as a resource for individuals looking to enhance their expertise in AI through various courses offered by OpenCV. The repository includes a wide range of topics such as image inpainting, instance segmentation, robotics, deep learning models, and more, providing practical implementations and code examples for readers to explore and learn from.

Taipy is an open-source Python library for easy, end-to-end application development, featuring what-if analyses, smart pipeline execution, built-in scheduling, and deployment tools.

PyTorch Lightning is a framework for training and deploying AI models. It provides a high-level API that abstracts away the low-level details of PyTorch, making it easier to write and maintain complex models. Lightning also includes a number of features that make it easy to train and deploy models on multiple GPUs or TPUs, and to track and visualize training progress. PyTorch Lightning is used by a wide range of organizations, including Google, Facebook, and Microsoft. It is also used by researchers at top universities around the world. Here are some of the benefits of using PyTorch Lightning: * **Increased productivity:** Lightning's high-level API makes it easy to write and maintain complex models. This can save you time and effort, and allow you to focus on the research or business problem you're trying to solve. * **Improved performance:** Lightning's optimized training loops and data loading pipelines can help you train models faster and with better performance. * **Easier deployment:** Lightning makes it easy to deploy models to a variety of platforms, including the cloud, on-premises servers, and mobile devices. * **Better reproducibility:** Lightning's logging and visualization tools make it easy to track and reproduce training results.

Azure OpenAI demos is a repository showcasing various demos and use cases of Azure OpenAI services. It includes demos for tasks such as image comparisons, car damage copilot, video to checklist generation, automatic data visualization, text analytics, and more. The repository provides a wide range of examples on how to leverage Azure OpenAI for different applications and industries.

The Intel® AI Reference Models repository contains links to pre-trained models, sample scripts, best practices, and tutorials for popular open-source machine learning models optimized by Intel to run on Intel® Xeon® Scalable processors and Intel® Data Center GPUs. The purpose is to quickly replicate complete software environments showcasing the AI capabilities of Intel platforms. It includes optimizations for popular deep learning frameworks like TensorFlow and PyTorch, with additional plugins/extensions for improved performance. The repository is licensed under Apache License Version 2.0.

This repository provides a comprehensive guide on building AI WhatsApp bots using Python and Flask. It covers setting up a Meta developer account, integrating webhook events for real-time message reception, and using OpenAI for AI responses. The tutorial includes steps for selecting phone numbers, sending messages with the API, configuring webhooks, integrating AI into the application, and adding a phone number. It also explains the process of creating a system user, obtaining access tokens, and validating verification requests and payloads for webhook security. The repository aims to help users create intelligent WhatsApp bots with Python and AI capabilities.

AI System School is a curated list of research in machine learning systems, focusing on ML/DL infra, LLM infra, domain-specific infra, ML/LLM conferences, and general resources. It provides resources such as data processing, training systems, video systems, autoML systems, and more. The repository aims to help users navigate the landscape of AI systems and machine learning infrastructure, offering insights into conferences, surveys, books, videos, courses, and blogs related to the field.

A movie and TV show , Songs & Books recommendation assistant for various streaming platforms.

Recommends self-hosted IT solutions, tailored for professionals (from https://awesome-selfhosted.net/)

Recomenda programas para fluxogramas e ilustra visualmente.

Ethics-focused GPT builder assistant based on European AI guidelines, recommendations and regulations

Given a set of Parameters, Provides a set of Order Recommendations

나에게 맞는 직업과 진로 추천받기. 적성에 맞는 진로 상담, 직업추천, 휴먼디자인, Based on Human Design System

Focused solely on tech in the legal system. No unrelated advice or recommendations.

意思決定が必要な時に、３基のスーパーコンピュータが多数決で決めてくれます

Expert in macroeconomics, providing holistic and system-wide insights.

Expert in residential speaker systems, offering detailed advice and product recommendations.

Interact with the HVAC matrix

Red Team Recipe and Guide for Fun & Profit.

A counselor aiding in writing recommendation letters for PhD applications, with a formal and informative tone.

Expert in drafting recommendation letters for U.S. green card application under EB-1A and EB2-NIW applications. Please start from the conversation starters but with the info of yourself.

为您找到想要的酒店，请说您的要求Finds hotels in China with preferd demand

Ask me to recommend a plugin or script from the official Hex-Rays plugin repository

Finds the best Shopify app for your requirements and budget

The best non-fiction book recommendation assistant.

Expert on Twitter's recommendation algorithm with code insights.